| Summary: | Medical disease classification using machine learning algorithms is a challenging task due to the nature of data, which can contain incomplete, uncertain, and imprecise information. The availability of such information in the dataset affects the performance of the classification model. In this paper, a Linguistic Neuro-Fuzzy with Feature Extraction (LNF-FE) model is utilized for the analysis of medical data for disease classification. Initially, this model uses a linguistic fuzzification process to generate membership values that handle the uncertainty problems. These membership values may not significantly contribute to the model, but it will increase the dimensions, for which more time will be required to train the model. To address this issue, Feature Extraction (FE) algorithms are hybridized in the Neuro-Fuzzy (NF) model to extract only those features (a reduced feature set) that are significantly contributing to the network. These reduced features are again passed to the Artificial Neural Network (ANN) model for classification. This proposed model is tested and validated through eight benchmark datasets, and the performance is compared with other models. The obtained results were tested using statistical techniques such as Friedman and Holm-Bonferroni for the proof of correctness. This experimental analysis shows that our proposed model outperforms better as compared to other models for solving real-world problems. Keywords: Disease classification, Machine learning, Neuro-fuzzy, Feature extraction, PCA
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